DMCA
Reliable 3D Surface Acquisition, Registration and Validation using Statistical Error Models (2001)
| Citations: | 8 - 0 self |
BibTeX
@MISC{Guehring01reliable3d,
author = {Jens Guehring},
title = {Reliable 3D Surface Acquisition, Registration and Validation using Statistical Error Models},
year = {2001}
}
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Abstract
We present a complete data acquisition and processing chain for the reliable inspection of industrial parts considering anisotropic noise. Data acquisition is performed with a stripe projection system that was modeled and calibrated using photogrammetric techniques. Covariance matrices are attached individually to points during 3D coordinate computation. Different datasets are registered using a new multi-view registration technique. In the validation step, the registered datasets are compared with the CAD model to verij that the measured part meets its specification. While previous methods have only considered the geometrical discrepancies between the sensed part and its CAD model we also consider statistical information to decide whether the differences are significant.
Keyphrases
statistical error model surface acquisition cad model data acquisition registered datasets coordinate computation industrial part previous method processing chain geometrical discrepancy measured part reliable inspection anisotropic noise validation step statistical information new multi-view registration technique covariance matrix different datasets photogrammetric technique stripe projection system sensed part complete data acquisition
